Update export_to_onnx.py

export_to_onnx.py

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+import torch
+from transformers import AutoModel, AutoProcessor
+from PIL import Image
+import requests
+import os
+import onnxruntime as ort
+import numpy as np
+
+# Constants
+MODEL_NAME = "amaye15/DaViT-Florence-2-large-ft"
+CACHE_DIR = os.getcwd()
+PROMPT = "<OCR>"
+IMAGE_URL = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
+ONNX_MODEL_PATH = "model.onnx"
+
+# Load the model and processor
+model = AutoModel.from_pretrained(MODEL_NAME, trust_remote_code=True, cache_dir=CACHE_DIR)
+processor = AutoProcessor.from_pretrained(MODEL_NAME, trust_remote_code=True, cache_dir=CACHE_DIR)
+
+# Prepare the input
+image = Image.open(requests.get(IMAGE_URL, stream=True).raw)
+inputs = processor(text=PROMPT, images=image, return_tensors="pt")
+
+# Export the model to ONNX
+input_names = ["pixel_values"]
+output_names = ["output"]
+torch.onnx.export(
+ model,
+ inputs["pixel_values"],
+ ONNX_MODEL_PATH,
+ input_names=input_names,
+ output_names=output_names,
+ dynamic_axes={"pixel_values": {0: "batch_size"}, "output": {0: "batch_size"}},
+ opset_version=11
+)
+
+# Load the ONNX model
+ort_session = ort.InferenceSession(ONNX_MODEL_PATH)
+
+# Prepare the inputs for ONNX model
+ort_inputs = {"pixel_values": inputs["pixel_values"].numpy()}
+
+# Run the ONNX model
+ort_outs = ort_session.run(None, ort_inputs)
+
+# Display the output
+print(ort_outs)